project structure | added image processing (cropping, background remove)

.gitignore

@@ -2,5 +2,4 @@
 .vsc
 .venv
 
-./data/*
-!./data/.gitkeep
+data

requirements.txt

@@ -0,0 +1 @@
+opencv-python

src/concept.py

@@ -0,0 +1,10 @@
+from enum import Enum, auto
+
+class Concept(Enum):
+	UNKNOWN = auto()
+	VORFAHRT_GEWAEHREN = auto()
+	VORFAHRT_STRASSE = auto()
+	STOP = auto()
+	RECHTS_ABBIEGEN = auto()
+	LINKS_ABBIEGEN = auto()
+	# TODO: add remaining signs

src/featue_extraction.py

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+class FeatureExtractor:
+
+	def get_overall_color_percentage():
+		pass
+
+	def get_color_per_raster_percentage():
+		pass
+

src/feature_vector.py

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+from typing import Any
+
+from concept import Concept
+
+
+class FeatureVector:
+
+	def __init__(self, concept: Concept, features: dict = {}) -> None:
+		self.features: dict = features
+		self.concept: Concept = concept
+
+
+	def add_feature(self, key, value) -> None:
+		self.features.update({key: value})
+
+
+	def get_concept(self) -> Concept:
+		return self.concept
+
+
+	def get_num_features(self) -> int:
+		return len(self.features)
+
+
+	def get_feature_value(self, key) -> Any:
+		return self.features[key]
+

src/image_processing.py

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+import cv2 as cv
+import numpy as np
+
+def crop(image: cv.Mat) -> cv.Mat:	
+	width, height = image.shape[:2]
+
+	x1 = None
+	for x in range(width):
+		for y in range(height):
+			b, g, r = image[x][y]
+			if(r < 150 and g < 150 and b < 150):
+				x1 = x
+				break
+		if x1 is not None:
+			break
+
+	y1 = None
+	for y in range(height):
+		for x in range(width):
+			b, g, r = image[x][y]
+			if(r < 150 and g < 150 and b < 150):
+				y1 = y
+				break
+		if y1 is not None:
+			break
+	
+	x2 = None
+	for x in range(width-1, 0, -1):
+		for y in range(height):
+			b, g, r = image[x][y]
+			if(r < 150 and g < 150 and b < 150):
+				x2 = x
+				break
+		if x2 is not None:
+			break
+
+	y2 = None
+	for y in range(height-1, 0, -1):
+		for x in range(width):
+			b, g, r = image[x][y]
+			if(r < 150 and g < 150 and b < 150):
+				y2 = y
+				break
+		if y2 is not None:
+			break
+	
+	return image[x1-2:x2+2, y1-2:y2+2]
+
+
+def remove_background(image: cv.Mat, lower_rgb: np.ndarray) -> cv.Mat:
+	width, height = image.shape[:2]
+
+	# left to right
+	for x in range(width):
+		for y in range(height):
+			b, g, r = image[x][y]
+			if(r >= lower_rgb[0] and g >= lower_rgb[1] and b >= lower_rgb[2]):
+				image[x][y] = [0, 0, 0]
+			else:
+				break
+	
+	# top to bottom
+	for y in range(height):
+		for x in range(width):
+			b, g, r = image[x][y]
+			if(r >= lower_rgb[0] and g >= lower_rgb[1] and b >= lower_rgb[2]):
+				image[x][y] = [0, 0, 0]
+			else:
+				break
+
+	# right to left
+	for x in range(width):
+		for y in range(height-1, 0, -1):
+			b, g, r = image[x][y]
+			if(r >= lower_rgb[0] and g >= lower_rgb[1] and b >= lower_rgb[2]):
+				image[x][y] = [0, 0, 0]
+			else:
+				break
+	
+	# bottom to top
+	for y in range(height):
+		for x in range(width-1, 0, -1):
+			b, g, r = image[x][y]
+			if(r >= lower_rgb[0] and g >= lower_rgb[1] and b >= lower_rgb[2]):
+				image[x][y] = [0, 0, 0]
+			else:
+				break
+
+	return image
+
+
+def raster_image(image: cv.Mat, num_cols: int = 4, num_rows: int = 4) -> dict[cv.Mat]:
+	pass

src/learner.py

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+class Learner:
+	# The training method, that changes the internal state of the learner such that 
+	# it will classify examples of a similar set (i.e. the testSet better.
+	# 
+	# @param trainingSet contains feature vectors and corresponding concepts 
+	# to provide experience to learn from  
+
+	def learn(trainingSet):
+		pass
+	
+	# find the concept of the example from the internal knowledge of the lerner
+	# this method must not consider example.getConcept() at all!!
+	# 
+	# @param example: is a feature vector
+	# @return the concept of the examplke as learned by this before
+	def classify(example):
+		pass